Abstract
Recommender systems (RS) have become an integral part of our daily life. However, most current RS often repeatedly recommend items to users
with similar profiles. We argue that recommendation should be diversified by leveraging session
contexts with personalized user profiles. For this,
current session-based RS (SBRS) often assume a
rigidly ordered sequence over data which does not
fit in many real-world cases. Moreover, personalization is often omitted in current SBRS. Accordingly, a personalized SBRS over relaxedly ordered
user-session contexts is more pragmatic. In doing so, deep-structured models tend to be too complex to serve for online SBRS owing to the large
number of users and items. Therefore, we design
an efficient SBRS with shallow wide-in-wide-out
networks, inspired by the successful experience in
modern language modelings. The experiments on a
real-world e-commerce dataset show the superiority of our model over the state-of-the-art methods